Development of a scale to measure the psychosocial impact of assistive devices: lessons learned and the road ahead
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: In this paper the history of the development and validation of the PIADS is reviewed. Assistive devices (ADs) are extremely prevalent forms of health care intervention for persons who have a disability. There is a consensus that the AD field needs a reliable and valid measure of how users perceive the impact of ADs on their quality of life (QoL) and sense of well-being. The Psychosocial Impact of Assistive Devices Scale (PIADS) is a 26 item self-rating scale designed to fill this measurement gap. The challenges that we encountered are described in attempting to adequately conceptualize QOL impact, and operationalize it in a measure suitable for use with virtually all forms of AD. Current efforts to extend the validation of the PIADS are summarized. CONCLUSIONS: The study concludes by suggesting directions for future research and development of the scale. They include a richer examination of its conceptual relationships to other health care and rehabilitation outcome measures, and further investigation of its clinical utility. The PIADS is a reliable and valid tool that appears to have very significant power to predict AD abandonment and retention. It can and should be used both deductively and inductively to build, discover and test theory about the psychosocial impact of assistive technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it